# Introduction In this challenge, we're focusing on implementing the early stopping technique for machine learning models. Early stopping is a method that halts training to avoid overfitting. It does so by monitoring the model's performance on a validation set to determine the optimal stopping point. We'll work with a list of loss values from the validation set for each training epoch and a given "patience" value. Our objective is to identify the epoch where the model performs the best, taking into account the patience value and stopping criteria. During training, a model's weights are fine-tuned to improve its accuracy on the training data. In the initial stages, this improvement often corresponds to better results on the validation set. However, as training advances, there's a risk of the model overfitting the training data, leading to reduced performance on the validation set. Early stopping helps us navigate this challenge.
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